Evidence map›Paper›PMID 42715288›Full record

ArticlePloS one2026

Mathematical modeling of HBV-HDV transmission with superinfection: The role of vaccination and liver transplantation in disease dynamics.

Dipo Aldila, Nur Syam Rahman, Anymore Majere, Fatmawati

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Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Dipo AldilaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia.ORCID https://orcid.org/0000-0001-9022-1701
Nur Syam RahmanDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia.
Anymore MajereDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia.ORCID https://orcid.org/0009-0006-4648-4081
FatmawatiDepartment of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatitis D virus (HDV) infection can substantially worsen the clinical outcomes of individuals infected with hepatitis B virus (HBV), making the prevention of HBV infection and the management of severe HBV-HDV cases important public-health priorities. This study introduces a novel mathematical model for Hepatitis B virus (HBV) and D virus (HDV) transmission, incorporating vaccination and liver transplantation as disease control strategies. In contrast to models that allow direct HDV infection or general coinfection pathways, the proposed model assumes that HDV occurs only through superinfection among individuals already infected with HBV. The model is constructed as a system of ordinary differential equations, where the human population is divided into susceptible, vaccinated, HBV-infected, recovered, chronic, and HDV-infected compartments. Model analysis reveals the existence and stability of the disease-free equilibrium, which exists and is asymptotically stable if the basic reproduction number (ℛ0) is less than one, and becomes unstable if it exceeds one. Two types of endemic equilibria are identified when ℛ0>1, corresponding to the absence and presence of HDV infection. Continuation analysis using MatCont reveals the existence of two branching points that govern the transition of stability between these equilibria, highlighting the conditions under which HDV emerges and persists. Global sensitivity analysis using Partial Rank Correlation Coefficient combined with Latin Hypercube Sampling indicates that vaccination coverage and vaccine efficacy play a dominant role in reducing ℛ0. Although liver transplantation does not affect the magnitude of ℛ0, it significantly influences the disease dynamics by reducing the outbreak size and delaying the timing of HBV and HDV outbreaks. These findings indicate that vaccination is essential for reducing transmission, whereas liver transplantation remains important for mitigating severe disease burden; thus, coordinated use of preventive and clinical interventions is needed for effective HBV-HDV control.

Indexed as

Hepatitis BHepatitis B virusHepatitis DHepatitis Delta VirusLiver TransplantationModels, TheoreticalSuperinfectionVaccinationCoinfectionHumans

Identifiers

PMID42715288
PMCPMC13557504

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.